A method for locating regions containing neural activation at a given confidence level from MEG data

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The MEG inverse problem does not have a general, unique solution. Unless restrictive model assumptions are made, there are generally many more free parameters than measurements and there exist silent sources - current distributions which produce no external magnetic field. By weighting solutions according to how well each fits our prior notion about what properties good solutions should have, it may be possible to obtain a single current distribution that best fits the data and our expectations. However, in general there will still exist a number of different current distributions which fit both the data and our prior expectations sufficiently ... continued below

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5 p.

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Schmidt, D.M. & George, J.S. February 1, 1996.

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The MEG inverse problem does not have a general, unique solution. Unless restrictive model assumptions are made, there are generally many more free parameters than measurements and there exist silent sources - current distributions which produce no external magnetic field. By weighting solutions according to how well each fits our prior notion about what properties good solutions should have, it may be possible to obtain a single current distribution that best fits the data and our expectations. However, in general there will still exist a number of different current distributions which fit both the data and our prior expectations sufficiently well. For example, a simulated data set based on a single or several dipoles can generally be fit equally well by a distributed current minimum-norm reconstruction. In experimental data it is often possible to find a relatively small number of dipoles which both fit the data and have a norm not much larger than that of the minimum-norm solution. Moreover, the few-dipole solutions often have currents in different regions than the corresponding minimum-norm solution. Because there exist well-fitting current distributions which may have current in significantly different locations, it can be misleading to infer locations of stimulus-correlated neural activity based on a single, best-fitting current distribution. we demonstrate here a method for inferring the location and number of regions containing neural activation by considering all possible current distributions within a given model (not just the most likely one) weighted according to how well each fits both the data and our prior expectations.

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5 p.

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OSTI as DE96012716

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  • Biomagnetism conference, Santa Fe, NM (United States), Feb 1996

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  • Other: DE96012716
  • Report No.: LA-UR--96-2057
  • Report No.: CONF-9602101--11
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 286162
  • Archival Resource Key: ark:/67531/metadc672622

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  • February 1, 1996

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  • June 29, 2015, 9:42 p.m.

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  • Oct. 3, 2017, 7:24 p.m.

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Schmidt, D.M. & George, J.S. A method for locating regions containing neural activation at a given confidence level from MEG data, article, February 1, 1996; New Mexico. (digital.library.unt.edu/ark:/67531/metadc672622/: accessed December 12, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.